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   "source": [
    "# Step-by-step Guidance for CIKM 2022 AnalytiCup Competition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1. 安装FederatedScope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-26T09:23:22.036523Z",
     "start_time": "2022-07-26T09:23:21.642646Z"
    }
   },
   "outputs": [],
   "source": [
    "# 拷贝FederatedScope并切换到比赛所使用的分支cikm22competition\n",
    "\n",
    "!cp -r fs_latest FederatedScope\n",
    "\n",
    "# 如果不在playground中请输入命令`cd FederatedScope`\n",
    "import os\n",
    "os.chdir('FederatedScope')\n",
    "\n",
    "!git checkout cikm22competition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2. 搭建运行环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-26T09:23:28.796456Z",
     "start_time": "2022-07-26T09:23:22.038843Z"
    }
   },
   "outputs": [],
   "source": [
    "# 安装 FS\n",
    "!pip install -e . --user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3. 下载比赛数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-26T09:23:29.189922Z",
     "start_time": "2022-07-26T09:23:28.798365Z"
    }
   },
   "outputs": [],
   "source": [
    "# 比赛数据集已经提前下载到`data`目录下，通过如下的命令进行解压:\n",
    "!mkdir -p data\n",
    "!cp -r ../data/CIKM22Competition ./data/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在比赛数据集已经被放置在`FederatedScope/data/CIKM22Competition`目录下，遵循`CIKM22Competition/${client_id}`的方式进行组织，其中`${client_id}`是client的序号并从1开始计数。每个client的目录下包含训练（train.pt），测试（test.pt）和验证（val.pt）三个部分的数据。可以通过`torch.load`的方式来查看数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "start_time": "2022-07-26T09:23:29.192273Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "# 加载序号为1的client的训练数据\n",
    "train_data_client1 = torch.load('./data/CIKM22Competition/1/train.pt')\n",
    "# 查看训练数据中的第一个训练样例\n",
    "print(train_data_client1[0])\n",
    "# 查看训练样例的label\n",
    "print(train_data_client1[0].y)\n",
    "# 查看训练样例的序号，即${sample_id}\n",
    "print(train_data_client1[0].data_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4. 在比赛数据上运行baseline算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FederatedScope为比赛数据内置了两个baseline算法：\"isolated training\"和\"FedAvg\"。假设你已经成功的搭建好FederatedScope的运行环境，并下载了比赛数据，那么可以通过以下的命令运行两个baseline算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-26T09:23:29.824268Z",
     "start_time": "2022-07-26T09:23:29.824254Z"
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   },
   "outputs": [],
   "source": [
    "# 作为示例，我们在这里仅运行3轮，用户可以按照自己的需求更改运行轮数\n",
    "# 按照如下命令运行isolated training\n",
    "!python federatedscope/main.py --cfg federatedscope/gfl/baseline/isolated_gin_minibatch_on_cikmcup.yaml --client_cfg federatedscope/gfl/baseline/isolated_gin_minibatch_on_cikmcup_per_client.yaml federate.total_round_num 3\n",
    "\n",
    "# 按照如下命令运行FedAvg\n",
    "!python federatedscope/main.py --cfg federatedscope/gfl/baseline/fedavg_gin_minibatch_on_cikmcup.yaml --client_cfg federatedscope/gfl/baseline/fedavg_gin_minibatch_on_cikmcup_per_client.yaml federate.total_round_num 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中参数--cfg xxxx.yaml指定了全局的参数，--client_cfg xxx.yaml为每一个client单独指定了参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5. 保存并提交预测结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 提交格式\n",
    "如CIKM 2022 AnalytiCup Competition介绍中所述，参赛者需要将所有client的预测结果保存到一个csv文件中进行提交。在这个csv文件中，每一行代表一个测试样例的预测结果，并且由`${client_id}`和`${sample_id}`标识所预测的测试样例。其中，`${client_id}`从1开始计数，`${sample_id}`需要与比赛数据中的测试样例的序号保持一致（可以通过测试样例的`data_index`属性获得测试样例的序号）。\n",
    "\n",
    "需要注意的是，分类任务和多维度回归任务分别遵循以下不同的格式：\n",
    "* 对分类任务，每一行应当遵循如下的格式(`${category_id}`从0开始计数)：\n",
    "\n",
    "```\n",
    "${client_id},${sample_id},${category_id}\n",
    "```\n",
    "\n",
    "* 对于N维的回归任务，每一行应当遵循如下的格式：\n",
    "```\n",
    "${client_id},${sample_id},${prediction_1st_dimension},…,${prediction_N-th_dimension}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 通过FederatedScope保存预测结果\n",
    "FederatedScope中的\"cikm22competition\"分支目前支持在训练结束时保存预测结果（相关代码见`federatedscope/gfl/trainer/graphtrainer.py`和`federatedscope/core/trainers/torch_trainer.py`）。\n",
    "预测结果将会被保存在一个名为prediction.csv的文件中，该文件位于`outdir`参数所指定的目录中。每次运行时，如果`outdir`所指定的目录已经存在，则会使用一个时间戳后缀用以区分。\n",
    "在训练结束时，FederatedScope的训练记录也将标识出预测结果所在的目录，如下所示\n",
    "![Finish saving prediction results](https://img.alicdn.com/imgextra/i1/O1CN01L68gve1nGu3BNp5vL_!!6000000005063-2-tps-4766-404.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 提交预测结果\n",
    "最后，参赛选手可以将预测结果下载到本地，并上传到[天池](https://tianchi.aliyun.com/competition/entrance/532008/introduction)。"
   ]
  }
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